1. Field
The invention relates to a method of detecting a floor obstacle using a laser range finder and in particular, to a method of detecting a relatively small floor obstacle existing on the driving path of a mobile robot using a laser range finder.
2. Description of Related Art
The combination of autonomous driving of a mobile robot to service filed is being studied. Under the environments, the classification of an area where the mobile robot from given environments can drive must be carried out preferentially for the safe driving of the mobile robot. An ability to detect the drivable area influences the safety of the robot as well as the driving performance, and prevents a driving-failure situation which makes the robot platform overturned.
A research about analysis on the driving-possibility of a mobile robot under given environment is being carried out. General method to analyze the driving-possibility is a terrain detection method based on occupancy grid map which is disclosed in a paper titled “High resolution maps from wide angle sonar (IEEE International Conference on Robotics and Automation, pp. 116-121, 1985)” by H. P. Moravec and A. Elfes. This is a method of analyzing a terrain using features of terrain data recorded in a grid cell of a 2D grid map.
In a paper titled “An efficient extension to elevation maps for outdoor terrain mapping and loop closing (The International Journal of Robotics Research, vol. 26, no. 2, pp. 217-230, 2007.)” by Patrick Pfaff, et al., an analysis of the driving-possibility and a terrain mapping are carried out using a 2D elevation map.
The analysis of the driving-possibility based on the grid map as mentioned above makes it possible to easily analyze lots of data when a concentrated 3D point cloud is formed. However, in case of using a 2D laser range finder, each grid cell has few distance information and thus, it is difficult to analyze data. Also, the size of the obstacle which can be detectable is limited due to the gird resolution.
Meanwhile, the detection range is limited in the indoor environment. Since the speed of the robot platform is limited, it is important to precisely detect the terrain near the mobile robot, rather than to detect a wide range of terrain. In case of a 3D laser range finder having wide detection range, it is difficult to detect a terrain near the mobile robot. It is also difficult to add an addition 3D range finder for observing only the terrain near the mobile robot due to a high price.
To solve this problem, it is useful to incline a 2D laser range finder towards the floor. The use of a 2D laser range finder makes it possible to precisely detect a terrain near the mobile robot and to concentrate the detection of the floor thereby data processing can be easily done.
In a paper titled “Traversable terrain classification for outdoor autonomous robots using single 2D laser scans (Integrated Computer-aided engineering, vol. 13, no. 3, pp. 223-232, 2006.)” by J. C. Andersen, et al., a method of detecting the drivable area using a inclined 2D laser range finder is suggested.
In a paper by J. C. Andersen, et al., features such as height, distribution, inclination, etc. of the measured data are extracted and then the drivable area is defined.
However, it is disadvantageous that the classification of the driving surface from the plurality of features depends on Heuristic threshold and thus, problematically, Heuristic constant must be redefined with respect to a certain environment or platform.
Meanwhile, a research about a method of classifying driving surface based on a learning method is being studied. In one example, Support Vector Machine (SVM) disclosed in a paper titled “Terrain classification and identification of tree stems using ground based LiDAR (Journal of Field Robotics, vol. 29, no. 6, pp. 891-910, 2012.)” by M. W. McDaniel, et al., is a most widely-being-used method for classifying data of surrounding environments.
Besides, a paper titled “Improving robot navigation in structured outdoor environments by identifying vegetation from laser data (IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1217-1222, 2009.) by Kai M. Wurm and a paper titled “Identifying vegetation from laser data in structured outdoor environments (Robotics and Autonomous Systems, vol. 62, no. 5, pp. 675-684, 2014.)” suggest a method of classifying terrain to identify area of trees and plants using the laser range finder.
Since the above mentioned method composes training data by using data of drivable area of the mobile robot as well as obstacle data, it has difficulty in collecting a number of data for obstacles of various types and sizes.
Also, relatively small obstacles located on the floor of the driving path cannot be easily detected by using the above mentioned method while relatively big obstacles such as people, desk, etc., on the driving path can be easily detected.
For example, garbage like a box paper which hinders the movement of the mobile robot or a tile protruded from the floor can be an obstacle which prevents the driving of the mobile robot.
As such, the situation can occur that an extremely small obstacle on the driving path prevents the robot platform from driving. However, the above mentioned methods cannot deal with the above situation.